Computational methods for analysis of cellular functions and pathways collectively targeted by differentially expressed microRNA.

This report presents computational methods of analysis of cellular processes, functions, and pathways affected by differentially expressed microRNA, a statistical basis of the gene enrichment analysis method, a modification of enrichment analysis method accounting for combinatorial targeting of Gene Ontology categories by multiple miRNAs and examples of the global functional profiling of predicted targets of differentially expressed miRNAs in cancer. We have also summarized an application of Ingenuity Pathway Analysis tools for in depth analysis of microRNA target sets that may be useful for the biological interpretation of microRNA profiling data. To illustrate the utility of these methods, we report the main results of our recent computational analysis of five published datasets of aberrantly expressed microRNAs in five human cancers (pancreatic cancer, breast cancer, colon cancer, lung cancer, and lymphoma). Using a combinatorial target prediction algorithm and statistical enrichment analysis, we have determined Gene Ontology categories as well as biological functions, disease categories, toxicological categories, and signaling pathways that are: targeted by multiple microRNAs; statistically significantly enriched with target genes; and known to be affected in specific cancers. Our recent computational analysis of predicted targets of co-expressed miRNAs in five human cancers suggests that co-expressed miRNAs provide systemic compensatory response to the abnormal phenotypic changes in cancer cells by targeting a broad range of functional categories and signaling pathways reportedly affected in a particular cancer.     

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.     

High-abundance mRNAs in Apis mellifera: comparison between nurses and foragers

A large volume of honey bee (Apis mellifera) tag-seq was obtained to identify differential gene expression via Solexa/lllumina Digital Gene Expression tag profiling (DGE) based on next generation sequencing. In total, 4,286,250 (foragers) and 3,422,327 (nurses) clean tags were sequenced, 24,568 (foragers) and 13,134 (nurses) distinct clean tags could not be match to the reference database, and 7508 and 6875 mapped genes were detected in foragers and nurses respectively. 7045 genes were found differentially expressed between foragers and nurses. Of those genes, 1621genes had significantly different expression, that is, they showed an expression ratio (foragers/nurses) of more than 2 and FDR (False Discovery Rate) of less than 0.001. We identified 101 genes that were uniquely expressed in foragers, and 9 genes that were only expressed in nurses. We performed the Gene Ontology (GO) category and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and found 415 genes with annotation terms linked to the GO cellular component category. 200 components of KEGG pathways were obtained, including 21 signaling pathways. The PPAR signaling pathway was the most highly enriched, with the lowest Q-value.     

Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. Availability: GCAT is freely available at http://binf1.memphis.edu/gcat.     

Porcine muscle sensory attributes associate with major changes in gene networks involving CAPZB, ANKRD1, and CTBP2

Principal component analysis of traits related to carcass and meat properties were combined with microarray expression data for the identification of functional networks of genes and biological processes taking place during the conversion of muscle to meat. Principal components (PCs) with high loadings of meat quality traits were derived from phenotypic data of 572 animals of a porcine crossbreed population. Microarray data of 74 M. longissimus dorsi samples were correlated with PC datasets. Lists of significantly correlated genes were analyzed for enrichment of functional annotation groups as defined in the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases as well as the Ingenuity Pathways Analysis library. Ubiquitination, phosphorylation, mitochondrion dysfunction, actin, integrin, platelet-derived growth factor, epidermal growth factor, vascular endothelial growth factor, and Ca signaling pathways are correlated with meat quality. Among the significantly trait-associated genes, CAPZB, ANKRD1, and CTBP2 are promoted as candidate genes for meat quality that provide a link between the highlighted pathways. Knowledge of the relevant biological processes and the differential expression of members of the pathway will provide tools that are predictive for traits related to meat quality and that may also be diagnostic for many muscle defects or damages including muscle atrophy, dystrophy, and hypoxia.     

A curated gene list for expanding the horizons of pigmentation biology

Over the past century, studies of human pigmentary disorders along with mouse and zebrafish models have shed light on the many cellular functions associated with visible pigment phenotypes. This has led to numerous genes annotated with the ontology term “pigmentation” in independent human, mouse, and zebrafish databases. Comparisons among these datasets revealed that each is individually incomplete in documenting all genes involved in integument‐based pigmentation phenotypes. Additionally, each database contained inherent species‐specific biases in data annotation, and the term “pigmentation” did not solely reflect integument pigmentation phenotypes. This review presents a comprehensive, cross‐species list of 650 genes involved in pigmentation phenotypes that was compiled with extensive manual curation of genes annotated in OMIM, MGI, ZFIN, and GO. The resulting cross‐species list of genes both intrinsic and extrinsic to integument pigment cells provides a valuable tool that can be used to expand our knowledge of complex, pigmentation‐associated pathways.     

Who tangos with GOA?-Use of Gene Ontology Annotation (GOA) for biological interpretation of '-omics' data and for validation of automatic annotation tools

The number of large-scale experimental datasets generated from high-throughput technologies has grown rapidly. Biological knowledge resources such as the Gene Ontology Annotation (GOA) database, which provides high-quality functional annotation to proteins within the UniProt Knowledgebase, can play an important role in the analysis of such data. The integration of GOA with analytical tools has proved to aid the clustering, annotation and biological interpretation of such large expression datasets. GOA is also useful in the development and validation of automated annotation tools, in particular text-mining systems. The increasing interest in GOA highlights the great potential of this freely available resource to assist both the biological research and bioinformatics communities.     

Two novel loci underlie natural differences in Caenorhabditis elegans abamectin responses

Parasitic nematodes cause a massive worldwide burden on human health along with a loss of livestock and agriculture productivity. Anthelmintics have been widely successful in treating parasitic nematodes. However, resistance is increasing, and little is known about the molecular and genetic causes of resistance for most of these drugs. The free-living roundworm Caenorhabditis elegans provides a tractable model to identify genes that underlie resistance. Unlike parasitic nematodes, C. elegans is easy to maintain in the laboratory, has a complete and well annotated genome, and has many genetic tools. Using a combination of wild isolates and a panel of recombinant inbred lines constructed from crosses of two genetically and phenotypically divergent strains, we identified three genomic regions on chromosome V that underlie natural differences in response to the macrocyclic lactone (ML) abamectin. One locus was identified previously and encodes an alpha subunit of a glutamate-gated chloride channel (glc-1). Here, we validate and narrow two novel loci using near-isogenic lines. Additionally, we generate a list of prioritized candidate genes identified in C. elegans and in the parasite Haemonchus contortus by comparison of ML resistance loci. These genes could represent previously unidentified resistance genes shared across nematode species and should be evaluated in the future. Our work highlights the advantages of using C. elegans as a model to better understand ML resistance in parasitic nematodes.     

Analysis of dysregulation of immune system in pancreatic cancer based on gene expression profile

The aim of this study was to explore the dysregulated expression of the immune system in pancreatic cancer and clarify the pathogenesis of pancreatic cancer. The Dataset GSE15471 was downloaded from GEO database, Student’s t test was used to screen differentially expressed genes (DEGs) between the pancreatic cancer group and the normal control group. Kyoto Encyclopedia of Genes and Genomes (KEGG) provides functional annotation was employed to explore the significant DEGs involved in biological functions. We got 988 significantly DEGs, including 832 up-regulated genes and 156 down-regulated genes. The ratio of up-regulated genes and down-regulated genes was 5.3. Total 13 biological pathways which were significant enriched with DEGs by KEGG pathway enrichment analysis. Finally, we constructed a overall network of the immune system in pancreatic cancer with these biological pathways information. Our study reveals that dysregulated pathways in pancreatic cancer associated with the immune system. Besides, we also identify some important molecular biomarkers of the pancreatic cancer, including CXCR4 and CD4. Dysfunctional pathways and important molecular biomarkers of pancreatic cancer will provide useful information for potential treatment of pancreatic cancer.     

Signalogs: orthology-based identification of novel signaling pathway components in three metazoans

Background

Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species.

Principal Findings

Here we introduce the concept of ‘signalog’ to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates.

Conclusions

Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs.     

A fast structural multiple alignment method for long RNA sequences

Background

Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.

Results

In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general.

Conclusion

GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.     

Caenorhabditis elegans,a pluricellular model organism to screen new genes involved in mitochondrial genome maintenance

The inheritance of functional mitochondria depends on faithful replication and transmission of mitochondrial DNA (mtDNA). A large and heterogeneous group of human disorders is associated with mitochondrial genome quantitative and qualitative anomalies. Several nuclear genes have been shown to account for these severe OXPHOS disorders. However, in several cases, the disease-causing mutations still remain unknown.Caenorhabditis elegans has been largely used for studying various biological functions because this multicellular organism has short life cycle and is easy to grow in the laboratory. Mitochondrial functions are relatively well conserved between human and C. elegans, and heteroplasmy exists in this organism as in human. C. elegans therefore represents a useful tool for studying mtDNA maintenance. Suppression by RNA interference of genes involved in mtDNA replication such as polg-1, encoding the mitochondrial DNA polymerase, results in reduced mtDNA copy number but in a normal phenotype of the F1 worms. By combining RNAi of genes involved in mtDNA maintenance and EtBr exposure, we were able to reveal a strong and specific phenotype (developmental larval arrest) associated to a severe decrease of mtDNA copy number. Moreover, we tested and validated the screen efficiency for human orthologous genes encoding mitochondrial nucleoid proteins. This allowed us to identify several genes that seem to be closely related to mtDNA maintenance in C. elegans.This work reports a first step in the further development of a large-scale screening in C. elegans that should allow to identify new genes of mtDNA maintenance whose human orthologs will obviously constitute new candidate genes for patients with quantitative or qualitative mtDNA anomalies.     

An application of MeSH enrichment analysis in livestock

An integral part of functional genomics studies is to assess the enrichment of specific biological terms in lists of genes found to be playing an important role in biological phenomena. Contrasting the observed frequency of annotated terms with those of the background is at the core of overrepresentation analysis (ORA). Gene Ontology (GO) is a means to consistently classify and annotate gene products and has become a mainstay in ORA. Alternatively, Medical Subject Headings (MeSH) offers a comprehensive life science vocabulary including additional categories that are not covered by GO. Although MeSH is applied predominantly in human and model organism research, its full potential in livestock genetics is yet to be explored. In this study, MeSH ORA was evaluated to discern biological properties of identified genes and contrast them with the results obtained from GO enrichment analysis. Three published datasets were employed for this purpose, representing a gene expression study in dairy cattle, the use of SNPs for genome‐wide prediction in swine and the identification of genomic regions targeted by selection in horses. We found that several overrepresented MeSH annotations linked to these gene sets share similar concepts with those of GO terms. Moreover, MeSH yielded unique annotations, which are not directly provided by GO terms, suggesting that MeSH has the potential to refine and enrich the representation of biological knowledge. We demonstrated that MeSH can be regarded as another choice of annotation to draw biological inferences from genes identified via experimental analyses. When used in combination with GO terms, our results indicate that MeSH can enhance our functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species.     

Genome-wide identification and comprehensive analysis of Excretory/Secretory proteins in nematodes provide potential drug targets for parasite control

Nematodes are responsible for causing severe diseases in plants, humans and other animals. Infection is associated with the release of Excretory/Secretory (ES) proteins into host cytoplasm and interference with the host immune system which make them attractive targets for therapeutic use. The identification of ES proteins through bioinformatics approaches is cost- and time-effective and could be used for screening of potential targets for parasitic diseases for further experimental studies. Here, we identified and functionally annotated 93,949 ES proteins, in the genome of 73 nematodes using integration of various bioinformatics tools. 30.6% of ES proteins were found to be supported at RNA level. The predicted ES proteins, annotated by Gene Ontology terms, domains, metabolic pathways, proteases and enzyme class analysis were enriched in molecular functions of proteases, protease inhibitors, c-type lectin and hydrolases which are strongly associated with typical functions of ES proteins. We identified a total of 452 ES proteins from human and plant parasitic nematodes, homologues to DrugBank-approved targets and C. elegans RNA interference phenotype genes which could represent potential targets for parasite control and provide valuable resource for further experimental studies to understand host-pathogen interactions.